基于Web访问信息挖掘的推荐方法研究
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摘要
随着Internet和WWW的迅速发展，用户访问信息广泛、海量地遍及于其上，其从用户维、时间维、空间维、访问对象维等各方面详尽反映出用户的访问细节。通过对用户访问信息进行有效的数据挖掘，可以得到有关用户访问行为的知识，这些知识可以服务于Web站点的服务提供方和访问者。
对服务提供方而言，他们需要好的自动辅助设计工具，根据用户的访问兴趣知识动态地调整页面拓扑结构，改进现有的信息服务，开展有针对性的电子商务以更好地满足访问者的需求。对访问者而言，他们希望看到的是个性化的页面，希望得到更好的满足各自需求的服务，希望从具有类似访问兴趣的其他一些用户的访问行为知识中得到有价值的启发。
因此从大量的用户访问信息中，如何自动地、高效地提取这些知识，即Web访问信息知识发现，具有十分重要的现实意义，这也成为当前国际上受到广泛关注的，新兴的研究领域。
本文在Web访问信息挖掘中的群体自适应领域和个性化这两个领域进行了研究，取得的主要研究成果为：
1．Web站点的群体自适应领域：
1）面向Web广播的聚类：为解决如何组织Web播出集，以利用宽带广播网进行Web广播这样一个问题，本文提出一种新的聚类方法WebClustering。通过使用这种聚类方法，可以得出一个有价值的Web页面播出集合，并且通过形成分层索引页面来帮助用户更好地访问这个Web页面集合。
2）大项序列发现和互信息规则发现：
为了挖掘用户访问的序列特性，本文提出一种新的大项序列发现方法。该方法定义了一种新的用户访问事务文法，用于挖掘用户访问的序列特性；为了发现用户访问相关主题域，本文提出一种新的利用互信息规则发现方法发现相关主题域的方法，在发现到的规则基础上，相应的聚类算法被提出以发现相关主题域。发现的大项序列和相关主题域可以帮助Web站点的设计者更好地理解用户的访问行为，用于调整Web站点的结构或者用于站点内知识的再分布。
3）群体用户访问兴趣和兴趣导航行为模式发现：
为了挖掘出用户的访问兴趣，本文提出一种新的挖掘群体用户在一个页面上兴趣分布程度的方法。该方法利用了用户访问所具有的目的性，即用户对某种概念的兴趣，以得到单个用户的访问兴趣，然后通过叠加单个用户的访问兴趣来最终得到群体用户在一个页面上的兴趣分布。由于这种兴趣分布可以和用
With the rapid growth in Internet and WWW, the user’browsing information is becoming enormous and pervasive, which represents the user access details with the user dimension, time dimension, space dimension, and access object dimension. Mining the user access information, we can obtain the knowledge about user access manners, which can be used for the service providers and users.
For the service providers, they require the good automatic assistant design tools that can dynamically adjust web topology, improve service, and carry out personalization electronic commerce for users according to the user access interest knowledge. For users, they need the personalization web pages and special service and get the valuable recommendation from others who have similar interest. Therefore, how to automatically and effectively get the knowledge from the vast user access information, i.e. web usage mining, became a new and important research field in the world.
This dissertation addresses the researches of the web adaptive research field and the personalization research field in web usage mining. The contribution of the dissertation is as follow:
1. The web adaptive research field:
1) Clustering on web broadcast: To resolve the information broadcast problems in broadband network, this dissertation presents a new web mining approach– WebClustering, which can make a new and valuable web broadcast set, and some layered index web pages generated to help users access the set.
2) Discovering large sequences and mutual information rules:
To mine the large sequences in user access sequences, this dissertation presents an approach that discovers the large sequence. It defines a kind of the new user access transaction grammar to get the user sequence access transactions from the user access transactions. To mine the mutuality themes, the mutual information discovering approach is used and a new clustering approach is given. The knowledge can help the web site designers deeply understand users’access in order to adjust the web site structures.
对服务提供方而言，他们需要好的自动辅助设计工具，根据用户的访问兴趣知识动态地调整页面拓扑结构，改进现有的信息服务，开展有针对性的电子商务以更好地满足访问者的需求。对访问者而言，他们希望看到的是个性化的页面，希望得到更好的满足各自需求的服务，希望从具有类似访问兴趣的其他一些用户的访问行为知识中得到有价值的启发。
因此从大量的用户访问信息中，如何自动地、高效地提取这些知识，即Web访问信息知识发现，具有十分重要的现实意义，这也成为当前国际上受到广泛关注的，新兴的研究领域。
本文在Web访问信息挖掘中的群体自适应领域和个性化这两个领域进行了研究，取得的主要研究成果为：
1．Web站点的群体自适应领域：
1）面向Web广播的聚类：为解决如何组织Web播出集，以利用宽带广播网进行Web广播这样一个问题，本文提出一种新的聚类方法WebClustering。通过使用这种聚类方法，可以得出一个有价值的Web页面播出集合，并且通过形成分层索引页面来帮助用户更好地访问这个Web页面集合。
2）大项序列发现和互信息规则发现：
为了挖掘用户访问的序列特性，本文提出一种新的大项序列发现方法。该方法定义了一种新的用户访问事务文法，用于挖掘用户访问的序列特性；为了发现用户访问相关主题域，本文提出一种新的利用互信息规则发现方法发现相关主题域的方法，在发现到的规则基础上，相应的聚类算法被提出以发现相关主题域。发现的大项序列和相关主题域可以帮助Web站点的设计者更好地理解用户的访问行为，用于调整Web站点的结构或者用于站点内知识的再分布。
3）群体用户访问兴趣和兴趣导航行为模式发现：
为了挖掘出用户的访问兴趣，本文提出一种新的挖掘群体用户在一个页面上兴趣分布程度的方法。该方法利用了用户访问所具有的目的性，即用户对某种概念的兴趣，以得到单个用户的访问兴趣，然后通过叠加单个用户的访问兴趣来最终得到群体用户在一个页面上的兴趣分布。由于这种兴趣分布可以和用
With the rapid growth in Internet and WWW, the user’browsing information is becoming enormous and pervasive, which represents the user access details with the user dimension, time dimension, space dimension, and access object dimension. Mining the user access information, we can obtain the knowledge about user access manners, which can be used for the service providers and users.
For the service providers, they require the good automatic assistant design tools that can dynamically adjust web topology, improve service, and carry out personalization electronic commerce for users according to the user access interest knowledge. For users, they need the personalization web pages and special service and get the valuable recommendation from others who have similar interest. Therefore, how to automatically and effectively get the knowledge from the vast user access information, i.e. web usage mining, became a new and important research field in the world.
This dissertation addresses the researches of the web adaptive research field and the personalization research field in web usage mining. The contribution of the dissertation is as follow:
1. The web adaptive research field:
1) Clustering on web broadcast: To resolve the information broadcast problems in broadband network, this dissertation presents a new web mining approach– WebClustering, which can make a new and valuable web broadcast set, and some layered index web pages generated to help users access the set.
2) Discovering large sequences and mutual information rules:
To mine the large sequences in user access sequences, this dissertation presents an approach that discovers the large sequence. It defines a kind of the new user access transaction grammar to get the user sequence access transactions from the user access transactions. To mine the mutuality themes, the mutual information discovering approach is used and a new clustering approach is given. The knowledge can help the web site designers deeply understand users’access in order to adjust the web site structures.
引文
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